Journal Article

Pepper leaf disease recognition based on enhanced lightweight convolutional neural networks

Zhang, Xin, Sun, Wenjing, Dai, Min, Miao, Hong, Wang, Lixing, Han, Liangxiu, Zhang, Shanwen, Dorjoy, Md Mehedi Hassan, Wang, Mingyou

2023

Overview

Pepper leaf disease identification based on convolutional neural networks (CNNs) is one of the interesting research areas. However, most existing CNN-based pepper leaf disease detection models are suboptimal in terms of accuracy and computing performance. In particular, it is challenging to apply CNNs on embedded portable devices due to a large amount of computation and memory consumption for leaf disease recognition in large fields. Therefore, this paper introduces an enhanced lightweight model based on GoogLeNet architecture. The initial step involves compressing the Inception structure to reduce model parameters, leading to a remarkable enhancement in recognition speed. Furthermore, the network incorporates the spatial pyramid pooling structure to seamlessly integrate local and global features. Subsequently, the proposed improved model has been trained on the real dataset of 9183 images, containing 6 types of pepper diseases. The cross-validation results show that the model accuracy is 97.87%, which is 6% higher than that of GoogLeNet based on Inception-V1 and Inception-V3. The memory requirement of the model is only 10.3 MB, which is reduced by 52.31%-86.69%, comparing to GoogLeNet. We have also compared the model with the existing CNN-based models including AlexNet, ResNet-50 and MobileNet-V2. The result shows that the average inference time of the proposed model decreases by 61.49%, 41.78% and 23.81%, respectively. The results show that the proposed enhanced model can significantly improve performance in terms of accuracy and computing efficiency, which has potential to improve productivity in the pepper farming industry.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Accuracy / Agricultural production / Algorithms / Artificial intelligence / Artificial neural networks / Computation / Computer memory / crop disease recognition / Crop diseases / Crops / Datasets / deep convolutional neural networks / Deep learning / Disease detection / Efficiency / GoogLeNet / Leaves / lightweight neural networks / Neural networks / Performance enhancement / Performance evaluation / Plant diseases / Plant Science / Portable equipment / real-time recognition / Recognition / Regularization methods / Rice